Monte Carlo Calculations in Nuclear Medicine
Bibliographic record
Abstract
Introduction to the Monte Carlo method (M Ljungberg). Variance reduction techniques (D R Haynor). Anthropomorphic phantoms (G Zubal). General Monte Carlo codes for use in medical radiation physics (P Andreo and M Ljungberg). An introduction to scintillation detector physics (P D Esser). The scintillation camera - basic principles (S-E Strand). The SIMSET program (T Lewellen). Vectorized Monte Carlo code for modelling photon transport in nuclear medicine (M F Smith). Positron emission tomography - basic principles (T Ohlsson and K Erlandsson). The SIMSPECT simulation system (M J Belanger et al). Monte Carlo simulation of photon transport in gamma camera collimators (D J de Vries and S C Moore). The SIMIND Monte Carlo program (M Ljungberg). Monte Carlo in SPECT scatter correction (K F Koral). Design of a collimator for imaging ^T111In (S C Moore et al). Estimation of the lung regions from Compton scatter data in SPECT (M A King and T-S Pan). The Monte Carlo method applied in other areas of SPECT imaging (M Ljungberg). Positron emission tomography: basic principles (K Erlandsson and T Ohlsson). PETSIM: Monte Carlo simulation of positron imaging systems (C J Thompson and Y Picard). Monte Carlo in quantitative 3D PET: Scatter (M Dahlbom and L Eriksson). The Monte Carlo method in other topics of nuclear medicine and medical physics (M Ljungberg). Contributors: Dr Dan DeVries, U Mass, Worcester Dr S C Moore, V A Medical Center, MA C J Thompson, McGill U, Canada Dr Pedro Andreo, IAEA and Stockholm, Vienna, Austria Dr Ken Koral, U Michigan Ann Arbor, Dr S P Mueller, Essen University Hospital, Germany Dr Marie Kijewski, Brigham and Women's Hospital, Boston, US George Zubal, Yale U, School of Medicine Dr Mike King, U Mass Medical School, Worcester MA, US Drs Miyaoka and Harrison, U Washington Medical Center, Seattle, US Dr M Dahlbom, UCLA School of Medicine, US L Eriksson, Karolinska Institute (so is Andreo) Sweden
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".